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WO2021162490A2 - Méthode de prédiction de score calcique et de maladie - Google Patents

Méthode de prédiction de score calcique et de maladie Download PDF

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Publication number
WO2021162490A2
WO2021162490A2 PCT/KR2021/001849 KR2021001849W WO2021162490A2 WO 2021162490 A2 WO2021162490 A2 WO 2021162490A2 KR 2021001849 W KR2021001849 W KR 2021001849W WO 2021162490 A2 WO2021162490 A2 WO 2021162490A2
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WIPO (PCT)
Prior art keywords
ray image
region
calcium
calcium score
disease
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Ceased
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PCT/KR2021/001849
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English (en)
Korean (ko)
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WO2021162490A3 (fr
Inventor
권준명
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Bodyfriend Co Ltd
Medical AI Co Ltd
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Bodyfriend Co Ltd
Medical AI Co Ltd
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Publication of WO2021162490A2 publication Critical patent/WO2021162490A2/fr
Publication of WO2021162490A3 publication Critical patent/WO2021162490A3/fr
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/503—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to methods of predicting calcium score and disease.
  • Calcium score (Coronary Artery Calcium Score; CACS) is a calcification index of the coronary arteries, and refers to an index quantified by measuring the amount of calcium deposited in the coronary arteries.
  • Coronary arteries are arterial blood vessels that supply oxygen and nutrients to the heart muscle.
  • Calcium score is used as a predictor of cardiovascular disease because it can cause various cardiovascular diseases such as coronary artery disease, myocardial infarction, angina pectoris, and ischemic heart disease due to insufficient supply.
  • a calcium score is obtained by analyzing a CT image (or a CT image, computerized tomography (CT) image) taken of a patient's heart region.
  • CT computerized tomography
  • An object of the present invention is to provide a calcium score prediction model for predicting a calcium score of an object based on an X-ray image (or an X-ray image, an X-ray image, an X-ray image, or an X-ray image) of the object.
  • Another object of the present invention is to provide a cardiovascular disease prediction model that predicts a disease (or cardiovascular disease) of an object based on an X-ray image of the object.
  • a method for predicting a calcium score and a disease for achieving the above-described technical problem includes an X-ray image of an object, a photographing time of the X-ray image, and identification information of the object. Acquiring a calcium score of any one of the calcium scores of a plurality of objects based on the imaging time and the identification information as a calcium score of the object (coronary artery calcium score; CACS), and reference calcium Acquiring a reference calcium score corresponding to the calcium score of the object from among the scores, acquiring disease information matched to the reference calcium score as disease information of the object, the X-ray image, the calcium score of the object and generating a training data set by matching the disease information of the object, and learning a predictive model for predicting a calcium score and a disease based on the training data set.
  • CACS coronary artery calcium score
  • a deep learning-based calcium score or cardiovascular disease prediction program using an X-ray image is combined with hardware to execute the above-mentioned calcium score or cardiovascular disease prediction method, and is stored in a medium .
  • the time of analyzing the CT image of the object and at least one through the CT image It is possible to shorten the forecasting time for predicting at least one of the object's calcium score and disease through the infrared image of the object rather than the CT image of the object.
  • FIG. 1 shows a disease prediction system according to an embodiment of the present invention.
  • FIG. 2 is a block diagram illustrating an apparatus for predicting a disease according to an embodiment of the present invention.
  • FIG. 3 illustrates an example for explaining a learning operation of a processor according to an embodiment of the present invention.
  • FIG. 4 is a flowchart illustrating an operation of generating a learning data set of the processor shown in FIG. 3 .
  • FIG. 5A shows an example for explaining an operation of determining a brightness value of the processor shown in FIG. 4 .
  • FIG. 5B is another example for explaining an operation of determining a brightness value of the processor shown in FIG. 4 .
  • FIG. 6 illustrates an example for explaining an operation of predicting a calcium score of a processor through an embodiment of the present disclosure.
  • FIG. 7 shows another example for explaining a learning operation of a processor according to an embodiment of the present invention.
  • FIG. 8 illustrates an example for explaining a disease prediction operation of a processor through an embodiment of the present disclosure.
  • a method for predicting a calcium score and a disease comprising: obtaining an X-ray image of an object, a photographing time of the X-ray image, and identification information of the object; obtaining a calcium score of any one of the calcium scores of a plurality of objects as a calcium score (coronary artery calcium score; CACS) of the object based on the photographing time point and the identification information; obtaining a reference calcium score corresponding to the calcium score of the subject from among the reference calcium scores; acquiring disease information matched to the reference calcium score as disease information of the object; generating a learning data set by matching the X-ray image, the calcium score of the object, and the disease information of the object; and learning a predictive model for predicting a calcium score and a disease based on the training data set.
  • spatially relative terms “below”, “beneath”, “lower”, “above”, “upper”, etc. It can be used to easily describe the correlation between a component and other components.
  • a spatially relative term should be understood as a term that includes different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.
  • FIG. 1 shows a disease prediction system according to an embodiment of the present invention.
  • the disease prediction system 10 includes an information providing device 100 , an image providing device 300 , and a disease prediction device 500 .
  • the information providing apparatus 100 provides a plurality of X-ray images of an object, a photographing time of each of the plurality of X-ray images, a plurality of CT images of the object, a photographing time of each of the plurality of CT images, and identification information of the object. It may be stored in a database (DB) of the information providing apparatus 100 .
  • the object may be an object (or an existing patient) that has performed a health examination.
  • the object identification information may be various pieces of information that can identify each object, such as a name and/or identification (ID) of each object.
  • the information providing apparatus 100 may match each X-ray image, a photographing time point of each X-ray image, and identification information on an object and store it in a database.
  • Each X-ray image may be a chest X-ray image of the chest of the object.
  • a photographing time of each X-ray image may be a time of photographing the chest of an object in order to generate each X-ray image.
  • the imaging times of the X-ray images may be different time points and different dates.
  • the information providing apparatus 100 may match each CT image, a photographing time point of each CT image, and identification information on an object and store it in a database.
  • Each CT image may be a chest CT image of the chest of the object.
  • the imaging time of each CT image may be a timing of imaging the chest of the object in order to generate each CT image.
  • the time points of the CT images may be different from each other and may be on different dates.
  • Each of the plurality of CT images stored in the information providing apparatus 100 may be a CT image generated by being photographed at the same or similar point in time to an X-ray image of any one of the plurality of X-ray images.
  • the similar time point may be the same date as the photographing time point of any one X-ray image.
  • the information providing apparatus 100 may store the calcium score of each CT image in a database.
  • the calcium score of each CT image may be a calcium score calculated by analyzing each CT image with a calcium score corresponding to each CT image.
  • the calcium score of each CT image is the amount of calcium deposited in the subject's cardiovascular system (eg, coronary arteries) at the same or similar time point as the time point of each CT image (or the time point of the X-ray image corresponding to each CT image). can represent
  • the information providing apparatus 100 may analyze each CT image based on a brightness value of a coronary artery region corresponding to the coronary artery of the object among the entire region of each CT image.
  • the information providing apparatus 100 may calculate a calcium score corresponding to each CT image based on the analysis result.
  • the information providing apparatus 100 may match each CT image and the calcium score of each CT image and store it in a database.
  • each CT image, the imaging time of each CT image, the calcium score of each CT image, and identification information of the object may be matched with each other and stored in the database.
  • the information providing apparatus 100 may store reference calcium scores in a database.
  • the reference calcium scores may be preset with various amounts of calcium that can be deposited in the coronary arteries.
  • the reference calcium scores may be calcium scores calculated by analyzing CT images of a plurality of objects.
  • the information providing apparatus 100 may analyze each reference calcium score and each reference calcium score, match the diagnosed disease information, and store it in the database.
  • each disease information may be information on a disease diagnosed based on each reference calcium score.
  • each disease information may be determined differently according to the high and low of each reference calcium score.
  • each disease information may be information about a disease possessed by or to develop in any subject according to the matched reference calcium score.
  • each disease information includes at least one of information on whether an arbitrary object has a disease, information on a disease possessed by an arbitrary object, and information on a probability that an arbitrary object will develop a disease.
  • the information on the disease possessed by any subject may vary, such as the name of the disease and/or the severity of the disease.
  • Information on a disease to be developed in an arbitrary subject may vary, such as the name of the disease to be developed and/or the probability that the disease will develop.
  • the disease is a cardiovascular disease highly correlated with calcium score, and may be various cardiovascular diseases such as angina pectoris, coronary artery disease such as myocardial infarction, valvular disease, heart failure, pericardial disease, hypertension, arteriosclerosis and/or cardiomyopathy.
  • cardiovascular diseases such as angina pectoris, coronary artery disease such as myocardial infarction, valvular disease, heart failure, pericardial disease, hypertension, arteriosclerosis and/or cardiomyopathy.
  • the information providing apparatus 100 transmits images (eg, X-ray images and CT images) and information (eg, identification information, imaging time, calcium score, and disease information) stored in the database to the disease prediction apparatus 500 .
  • images eg, X-ray images and CT images
  • information eg, identification information, imaging time, calcium score, and disease information
  • the above-described object may be a plurality of objects. Accordingly, the information providing apparatus 100 provides X-ray images for each of the plurality of objects, a photographing time of each X-ray image, CT images of each of the plurality of objects, a photographing time of each CT image, and calcium of each CT image. Scores can be stored in a database.
  • the image providing apparatus 300 may generate a target X-ray image of a target object and provide the target X-ray image and the imaging time of the target X-ray image to the disease prediction apparatus 500 .
  • the target object may be a single object as a target (or a new patient) for predicting at least one of a calcium score and a disease.
  • the target object may be any one object among the plurality of objects or an object different from the plurality of objects.
  • the target X-ray image is an X-ray image of the chest of the target object, and may be a chest X-ray image.
  • the imaging time of the target X-ray image may be a timing of photographing the chest of the target object in order to generate the target X-ray image.
  • the disease predicting apparatus 500 may learn a predictive model of the disease predicting apparatus 500 based on at least one of an X-ray image, a calcium score, and disease information obtained from the information providing apparatus 100 .
  • the disease prediction apparatus 500 may repeatedly learn the prediction model by using at least one of each X-ray image, a calcium score corresponding to each X-ray image, and disease information corresponding to each X-ray image.
  • the disease prediction apparatus 500 may predict (or diagnose) at least one of a calcium score and a disease of the target object based on the target X-ray image and the learned prediction model.
  • the disease prediction apparatus 500 may predict at least one of a calcium score and a disease of the target object without analyzing the CT image of the target object (or the CT image of the chest of the target object).
  • the disease prediction apparatus 500 shortens the prediction time, which is the time to take and generate a CT image, the time to analyze the CT image, and the time to predict at least one through the CT image when at least one of the calcium score and the disease is predicted. and can reduce the cost of forecasting used in forecasting.
  • the disease prediction device 500 increases the accessibility and convenience of cardiovascular health management by enabling the target object and/or the manager who manages the target object to manage the target object's cardiovascular health with only an X-ray image of the chest of the target object, It can reduce the cost of cardiovascular health care.
  • the calcium score calculation operation and the disease information determination operation are performed by the information providing apparatus 100, but are not limited thereto.
  • the disease prediction apparatus 500 may perform a calcium score calculation operation and a disease information determination operation using the database of the information providing apparatus 100 .
  • FIG. 2 is a block diagram illustrating an apparatus for predicting a disease according to an embodiment of the present invention.
  • the disease prediction apparatus 500 may be implemented as an electronic device.
  • the electronic device may be various devices such as a personal computer (PC), a server, a module, or a portable electronic device (or a personal electronic device, a user device).
  • Portable electronic devices include a laptop computer, a mobile phone, a smart phone, a tablet PC, a mobile internet device (MID), a personal digital assistant (PDA), and an enterprise digital assistant (EDA). ), digital still camera, digital video camera, PMP (portable multimedia player), PND (personal navigation device or portable navigation device), handheld game console, e-book (e-book) may be implemented as a smart device.
  • the smart device may be implemented as a smart watch or a smart band.
  • the disease prediction apparatus 300 may be implemented as a server.
  • the disease prediction apparatus 500 may include a memory 510 and a processor 530 .
  • the memory 510 may store instructions (or programs) executable by the processor 530 .
  • the instructions may include instructions for executing the operation of the disease prediction apparatus 500 and/or the operation of each component of the disease prediction apparatus 500 .
  • the processor 530 may process data stored in the memory 510 .
  • the processor 530 may execute computer readable codes (eg, software) stored in the memory 510 and instructions induced by the processor.
  • the processor 530 may be a hardware-implemented data processing device having a circuit having a physical structure for executing desired operations.
  • desired operations may include code or instructions included in a program.
  • a data processing device implemented as hardware includes a microprocessor, a central processing unit, a processor core, a multi-core processor, and a multiprocessor. , an Application-Specific Integrated Circuit (ASIC), and a Field Programmable Gate Array (FPGA).
  • ASIC Application-Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • the processor 530 may iteratively learn the predictive model based on each of the X-ray images of each object.
  • an operation in which the processor 530 learns a predictive model based on a single X-ray image will be described.
  • An operation to be described below may be applied to an operation of repeatedly learning a predictive model based on each object and each X-ray image.
  • FIG. 3 is a diagram illustrating an example for explaining a learning operation of the processor according to an embodiment of the present invention
  • FIG. 4 is a flowchart illustrating an operation of generating a learning data set of the processor shown in FIG. 3 .
  • the processor 530 may learn a predictive model for predicting a calcium score based on the X-ray image.
  • the processor 530 may obtain an X-ray image of an object, a photographing time of the X-ray image, and identification information of the object through the information providing apparatus 100 ( S310 ).
  • the object may be any one object among a plurality of objects.
  • the X-ray image may be any one X-ray image from among a plurality of X-ray images of any one object.
  • Any one X-ray image may be a chest X-ray image of the chest of any one object.
  • the imaging time of the X-ray image may be the time of photographing the chest of any one object in order to generate any one X-ray image.
  • the processor 530 may learn a predictive model based on an X-ray image capturing time and object identification information ( S330 ).
  • the prediction model may be a calcium score prediction model that predicts a calcium score through an X-ray image.
  • the processor 530 may acquire any one calcium score from among the calcium scores of a plurality of objects stored in the information providing apparatus 100 as the object's calcium score based on the time of capturing the X-ray image and the identification information of the object ( S331).
  • each of the calcium scores may be a calcium score calculated by analyzing each of the CT images for each object.
  • Any one calcium score may be a calcium score corresponding to an X-ray image of the object and identification information of the object among the calcium scores of the plurality of objects.
  • the calcium score of the object may indicate an amount of calcium deposited in the cardiovascular system (eg, coronary artery) of the object at the same or similar time point as the time point of the X-ray image.
  • the similar time point may be the same date (or the same day) as the photographing time point.
  • the processor 530 may determine a photographing time of an X-ray image of an object among CT images of a plurality of objects based on identification information of the plurality of objects and a photographing time of CT images of the plurality of objects. Any one CT image corresponding to the identification information of the object may be acquired.
  • the CT images may be chest CT images of the chests of the plurality of objects.
  • Each of the imaging times of the CT images may be a timing of imaging the chest of each object in order to generate each CT image.
  • the processor 503 may obtain CT images corresponding to the identification information of the object from among the CT images of the plurality of objects, as CT images of the object, based on the identification information of the plurality of objects.
  • the processor 530 may acquire any one CT image corresponding to the imaging timing of the X-ray image from among the CT images of the object based on imaging timings of the CT images of the object.
  • the imaging time of any one CT image may be the same as or similar to the imaging time of the X-ray image.
  • the processor 530 may determine the calcium score of any one CT image as the calcium score of the object.
  • the calcium score of any one CT image may be a calcium score calculated based on any one CT image.
  • the calcium score of any one CT image may indicate the amount of calcium deposited in the cardiovascular (or coronary artery) of the subject at the same or similar time point as the imaging time of any one CT image.
  • the processor 530 may generate a training data set by matching the X-ray image and the calcium score of the object ( S333 ).
  • the processor 530 may determine any one of the brightness values of the heart region corresponding to the heart of the object among the entire region of the X-ray image and the brightness values of the coronary artery region corresponding to the coronary artery of the heart region. may be determined as a feature value of the X-ray image of the object.
  • the brightness value of at least one of the heart region and the coronary artery region among the entire region may be different depending on the degree of calcium deposition in the coronary artery of the object. Accordingly, the processor 530 may determine any one of a brightness value of the heart region and a brightness value of the coronary artery region as a feature value of the X-ray image of the object.
  • the processor 530 may determine a brightness value of a heart region corresponding to the heart of the object among all regions of an X-ray image, and a brightness value of a coronary artery region corresponding to a coronary artery of the heart among the heart regions. (S333a).
  • the processor 530 may determine any one of a brightness value of the heart region and a brightness value of the coronary artery as a feature value of the X-ray image of the object ( S333b ).
  • the processor 530 may generate a training data set by matching the X-ray image of the object, the characteristic values of the X-ray image of the object, and the calcium score of the object ( S333c ).
  • the processor 530 may learn a predictive model based on the training data set ( S335 ).
  • the predictive model may be modeled (or built) with a deep learning algorithm for predicting a calcium score, but is not limited thereto.
  • the predictive model may be modeled through various techniques such as a forest (Random Forest), a support vector machine (Support Vector Machine), a machine learning technique, and/or a neural network.
  • the predictive model may be modeled (or built) through a recurrent neural network (RNN).
  • RNN recurrent neural network
  • the predictive model may be modeled (or constructed) through a multilayer perceptron (MLP), a convolutional neural network (CNN), or the like.
  • MLP multilayer perceptron
  • CNN convolutional neural network
  • long short-term memory may be applied to prevent performance degradation due to a long-range dependency vanishing gradient that may occur as the length of an event increases.
  • stochastic gradient descent SGD
  • momentum Adam
  • AdaGrad AdaGrad
  • RMSprop and the like may be used as a predictive model optimization technique.
  • the training data D can be learned only once, parameters that minimize the error function can be obtained through repeated epochs several times. have.
  • RNNs can output the hidden layer as an input to the same hidden layer.
  • the RNN is a neural network that has a memory capability by considering the current input data and the data received in the past at the same time and having a feedback structure.
  • the RNN can be trained to interpret the current data according to the meaning of the previous data in the data.
  • LSTM one of RNNs, also called long short term memory network, can learn long-term dependencies.
  • the prediction model is modeled through an arbitrary neural network capable of processing data, such as a depth gated RNN, a clockwork RNN, etc. as well as an LSTM, which is one of the RNNs. It can be (or built).
  • FIG. 5A shows an example for explaining an operation of determining a brightness value of the processor shown in FIG. 4 .
  • the processor 530 may determine the brightness value of the heart region of the X-ray image of the object.
  • the processor 530 may determine a region that does not correspond to the cardiac region among the entire region of the X-ray image and any one region among the cardiac region as a region of interest (ROI) (S333a-1).
  • the region not corresponding to the heart region may be a region corresponding to an organ (eg, lung, airway, bronchus, etc.) other than the heart and fracture fragments (eg, ribs) among the entire region.
  • the processor 530 may extract a cardiac X-ray image corresponding to the cardiac region from the X-ray image by pre-processing the X-ray image based on the region of interest ( S333a - 2 ).
  • the heart X-ray image which is the pre-processed X-ray image, may be an X-ray image processed only by the heart region of the X-ray image of the object.
  • the processor 530 may extract an image corresponding to the heart region from the X-ray image.
  • the processor 530 may determine the extracted image as a heart X-ray image.
  • the processor 530 may remove an image corresponding to the region that does not correspond to the heart region from the X-ray image.
  • the processor 530 may remove all regions that do not correspond to the cardiac region at once, but is not limited thereto.
  • the processor 530 sets the organs (eg, lung, airway, bronchus, etc.) and fracture fragments (eg, ribs) regions of the entire region except the heart region as regions of interest, and sequentially can be extracted and removed.
  • the processor 530 may determine an image in which a region not corresponding to the heart region is excluded from the X-ray image as the cardiac X-ray image.
  • the processor 530 may determine the brightness value of the cardiac X-ray image as the brightness value of the heart region ( S333a - 3 ). For example, the processor 530 may obtain a brightness value of each of the pixels included in the cardiac X-ray image and determine an average value of the obtained brightness values as the brightness value of the heart region. In other words, the brightness value of the heart region may be an average brightness value of pixels included in the heart X-ray image.
  • FIG. 5B is another example for explaining an operation of determining a brightness value of the processor shown in FIG. 4 .
  • the processor 530 may determine a brightness value of the coronary artery region. In this case, the processor 530 may perform an operation of determining the brightness value of the coronary artery region without performing steps S333a-1 and S333a-2 after performing steps S333a-1 and S333a-2 as described in FIG. 5A . .
  • the processor 530 may recrystallize any one of the heart region that does not correspond to the coronary artery region and the coronary artery region as the region of interest ( S333a - 4 ).
  • the region not corresponding to the coronary artery region may be a region corresponding to the heart region excluding the coronary artery in the heart region.
  • the processor 530 may extract a coronary X-ray image corresponding to the coronary artery from the heart X-ray image by pre-processing the heart X-ray image based on the re-determined region of interest ( S333a - 5 ).
  • the coronary X-ray image which is the pre-processed heart X-ray image, may be an X-ray image processed with only the coronary region of the heart X-ray image.
  • the processor 530 may extract an image corresponding to the coronary region from the cardiac X-ray image.
  • the processor 530 may determine the extracted image as a coronary X-ray image.
  • the processor 530 may remove an image corresponding to the region that does not correspond to the coronary region from the cardiac X-ray image.
  • the processor 530 may remove all regions that do not correspond to the coronary artery region at once, but is not limited thereto.
  • the processor 530 may set regions of the heart that do not correspond to the coronary region as regions of interest, and sequentially extract and remove them.
  • the processor 530 may determine an image from which a region not corresponding to the coronary artery is excluded from the cardiac X-ray image as the coronary X-ray image.
  • the processor 530 may determine the brightness value of the coronary X-ray image as the brightness value of the coronary artery region. For example, the processor 530 may obtain a brightness value of each of the pixels included in the coronary X-ray image and determine an average value of the obtained brightness values as the brightness value of the coronary artery region. In other words, the brightness value of the coronary artery region may be an average brightness value of pixels included in the coronary X-ray image.
  • the processor 530 performs a preprocessing process, but is not limited thereto.
  • the information providing apparatus 100 may provide the processor 530 with at least one X-ray image among an X-ray image of an object, a heart X-ray image, and a coronary X-ray image by performing the preposition process of the processor 530 . have.
  • FIG. 6 illustrates an example for explaining an operation of predicting a calcium score of a processor through an embodiment of the present disclosure.
  • the processor 530 may predict (or diagnose) the calcium score of the target object by inputting the target X-ray image of the target object into the learned prediction model.
  • the learned prediction model may be a calcium score prediction model.
  • the processor 530 may obtain a target X-ray image of the target object ( S610 ).
  • the target X-ray image is a chest X-ray image of the chest of the target object, and may be a chest X-ray image generated by imaging the chest of the target object at the time of capturing the target X-ray image.
  • the imaging time of the target X-ray image may be a timing of photographing the chest of the target object in order to generate the target X-ray image.
  • the processor 530 may predict the calcium score of the target object by inputting the target X-ray image to the learned prediction model (S630).
  • the calcium score of the target object may be a calcium score estimated by the learned prediction model from the target X-ray image.
  • the calcium score of the target object may indicate the amount of calcium deposited in the cardiovascular (or coronary artery) of the target object at the same or similar time point as the target X-ray image.
  • the processor 530 may pre-process the target X-ray image to extract at least one of a heart region and a coronary artery region of the target X-ray image.
  • the processor 530 may determine a brightness value of at least one area. The operation of determining the brightness value may be the same as described above.
  • the processor 530 may predict the calcium score of the target object by inputting the target X-ray image and at least one brightness value to the learned prediction model.
  • the processor 530 inputs the target X-ray image and at least one brightness value to the learned prediction model, but is not limited thereto.
  • the processor 530 may input the target X-ray image to the learned prediction model.
  • the processor 530 may cause the learned prediction model to analyze the target X-ray image to determine at least one brightness value, and then to predict the calcium score of the target object based on the at least one brightness value.
  • FIG. 7 shows another example for explaining a learning operation of a processor according to an embodiment of the present invention.
  • the processor 530 may learn a predictive model for predicting a disease based on the X-ray image.
  • the processor 530 may perform steps S710 and S731 that are the same as steps S310 and S310 as shown in FIG. 3 .
  • Step S710 is the same as step S310
  • step S731 is the same as step S331, so a detailed description will be omitted.
  • the processor 530 may perform step S733 without performing step S333.
  • the processor 530 may acquire disease information of the object based on the calcium score of the object (S733).
  • the processor 530 may obtain a reference calcium score corresponding to the calcium score of the object from among the reference calcium scores stored in the information providing device 100 .
  • the reference calcium scores may be preset with various amounts of calcium that can be deposited in the coronary arteries.
  • the reference calcium scores may be calcium scores calculated by analyzing CT images of a plurality of objects. The calcium score of the subject and the reference calcium score corresponding to the calcium score of the subject may be the same calcium score.
  • the processor 530 may acquire disease information matched to the obtained reference calcium score as disease information of the object.
  • the disease information of the object may be information about a disease that the object possesses (or possesses) or will develop in the object at the same or similar time point as when an X-ray image of the object is taken.
  • the processor 530 acquires the disease information of the object based on the calcium score of the object, but is not limited thereto.
  • the processor 530 may acquire disease information of the object based on a cardiovascular examination performed on the object at the same or similar time point as an X-ray image of the object as well as the calcium score of the object.
  • the cardiovascular examination is an examination performed on the same date as the time when the X-ray image of the object is taken, and may include at least one of an electrocardiogram examination, an exercise stress test, and an echocardiography examination.
  • the processor 530 may generate a learning data set by matching the X-ray image with the disease information of the object ( S735 ).
  • the processor 530 may perform steps S333a and S33b to determine any one of a brightness value of a heart region and a brightness value of a coronary artery of the X-ray image as a feature value of the X-ray image.
  • the processor 530 may generate a learning data set by matching the X-ray image, the characteristic value of the X-ray image, and the disease information of the object.
  • the processor 530 may learn a predictive model based on the training data set ( S335 ).
  • the predictive model may be modeled (or built) with a deep learning algorithm that predicts a disease, but is not limited thereto.
  • the predictive model may be modeled through the various techniques described above.
  • FIG. 8 illustrates an example for explaining a disease prediction operation of a processor through an embodiment of the present disclosure.
  • the processor 530 may predict (or diagnose) a disease of the target object by inputting the target X-ray image of the target object into the learned prediction model.
  • the learned prediction model may be a disease prediction model.
  • the processor 530 may acquire a target X-ray image of the target object (S810).
  • the processor 830 may input the target X-ray image into the disease prediction model (S830) to predict the disease of the target object (S850).
  • the disease of the target object may be a disease estimated by the learned prediction model from the target X-ray image.
  • the processor 530 may determine whether the target object has a disease (or cardiovascular disease) at the same or similar time point when the target X-ray image is taken through the prediction model to which the target X-ray image is input, and whether the target object has a disease (or cardiovascular disease). And it is possible to predict at least one of diseases that will develop in the target object after the same or similar time point.
  • a disease or cardiovascular disease
  • the processor 530 may pre-process the target X-ray image to extract at least one of a heart region and a coronary artery region of the target X-ray image.
  • the processor 530 may determine a brightness value of at least one area. The operation of determining the brightness value may be the same as described above.
  • the processor 530 may predict (or determine) a disease of the target object by inputting the target X-ray image and at least one brightness value into the learned prediction model.
  • the processor 530 inputs the target X-ray image and at least one brightness value to the learned prediction model, but is not limited thereto.
  • the processor 530 may input the target X-ray image to the learned prediction model.
  • the processor 530 may cause the learned prediction model to analyze the target X-ray image to determine at least one brightness value, and then to predict the disease of the target object based on the at least one brightness value.
  • the learned predictive model predicts a calcium score or disease, but is not limited thereto.
  • the processor 530 may generate a learning data set by matching an X-ray image of an object, a feature value of the X-ray image, a calcium score of the object, and disease information of the object.
  • the processor 530 may learn a predictive model for predicting a calcium score and a disease based on the training data set.
  • the predictive model may be modeled (or built) with a deep learning algorithm that predicts a calcium score and a disease, but is not limited thereto.
  • the predictive model may be modeled through various techniques such as a forest (Random Forest), a support vector machine (Support Vector Machine), a machine learning technique, and/or a neural network.
  • the processor 530 may predict a calcium score and a disease of the target object by inputting the target X-ray image of the target object into the learned prediction model.
  • the learned prediction model may be a calcium score and a disease prediction model.
  • the processor 530 may predict the calcium score and disease of the target object at the same or similar time point as the target X-ray image capturing time through the prediction model to which the target X-ray image is input. Since the above-described calcium score prediction operation and disease prediction operation may be applied to the prediction operation, a detailed description thereof will be omitted.
  • the method for predicting a calcium score and/or disease based on deep learning through a chest X-ray image is implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. can be saved.
  • the above-described program is C, C++, JAVA, machine language, etc. that a processor (CPU) of the computer can read through a device interface of the computer in order for the computer to read the program and execute the methods implemented as a program
  • It may include code (Code) coded in the computer language of Such code may include functional code related to functions defining functions necessary for executing the methods, etc. can do.
  • the code may further include additional information necessary for the processor of the computer to execute the functions or code related to memory reference for which location (address address) in the internal or external memory of the computer should be referenced. have.
  • the code uses the communication module of the computer to determine how to communicate with any other computer or server remotely. It may further include a communication-related code for whether to communicate and what information or media to transmit and receive during communication.
  • the storage medium is not a medium that stores data for a short moment, such as a register, a cache, a memory, etc., but a medium that stores data semi-permanently and can be read by a device.
  • examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program may be stored in various recording media on the various servers 10 accessible by the computer or in various recording media on the computer of the user.
  • the medium may be distributed in a computer system connected by a network, and a computer readable code may be stored in a distributed manner.

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Abstract

La présente invention concerne un procédé basé sur un apprentissage profond pour prédire un score calcique ou une maladie cardiovasculaire à l'aide d'une image radiographique de la poitrine, le procédé pouvant comprendre les étapes consistant à : obtenir une image radiographique d'un objet, un point de vue de capture de l'image radiographique et des informations d'identification de l'objet pour obtenir un score calcique (score calcique d'artère coronaire (CACS)) de l'objet et des informations de maladie de l'objet ; mettre en correspondance le score calcique de l'objet et les informations de maladie de l'objet pour générer un ensemble de données d'apprentissage ; et apprendre un modèle de prédiction qui prédit un score calcique ou une maladie sur la base de l'ensemble de données d'apprentissage générées.
PCT/KR2021/001849 2020-02-10 2021-02-10 Méthode de prédiction de score calcique et de maladie Ceased WO2021162490A2 (fr)

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CN116491938A (zh) * 2023-06-27 2023-07-28 亿慧云智能科技（深圳）股份有限公司 一种ecg无创血糖测量方法及系统
IT202300009270A1 (it) * 2023-05-09 2024-11-09 Univ Degli Studi Di Torino Sistema di rilevamento di calcio coronarico

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KR102825896B1 (ko) * 2022-12-15 2025-06-26 주식회사 엑스큐브 의료 영상 처리 방법 및 디바이스
CN117292180A (zh) * 2023-09-19 2023-12-26 澳门科技大学 一种基于机器学习的冠状动脉狭窄程度预测方法

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KR102032611B1 (ko) * 2017-08-23 2019-10-15 주식회사 메디웨일 Ct 영상을 이용하여 심혈관 병변을 판단하는 방법 및 애플리케이션
WO2019125026A1 (fr) * 2017-12-20 2019-06-27 주식회사 메디웨일 Procédé et appareil pour aider au diagnostic d'une maladie cardiovasculaire

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IT202300009270A1 (it) * 2023-05-09 2024-11-09 Univ Degli Studi Di Torino Sistema di rilevamento di calcio coronarico
WO2024231874A1 (fr) * 2023-05-09 2024-11-14 Universita' Degli Studi Di Torino Système de détection de calcium coronaire
CN116491938A (zh) * 2023-06-27 2023-07-28 亿慧云智能科技（深圳）股份有限公司 一种ecg无创血糖测量方法及系统
CN116491938B (zh) * 2023-06-27 2023-10-03 亿慧云智能科技（深圳）股份有限公司 一种ecg无创血糖测量方法及系统

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